References ========== HydroBayesCal implements and builds on the following work. Each item is available from its publisher via the DOI/link below. (For developer convenience the project keeps local copies in a git-ignored ``ExportedItems/`` folder; these are not redistributed with the package.) Core methodology ---------------- * **Oladyshkin, S., Mohammadi, F., Kroeker, I., & Nowak, W. (2020).** *Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory.* Entropy, 22(8), 890. `doi:10.3390/e22080890 `_ The Bayesian active-learning strategy (Bayesian model evidence and relative entropy as training-point selection criteria) at the heart of HydroBayesCal. * **Rasmussen, C. E., & Williams, C. K. I. (2006).** *Gaussian Processes for Machine Learning.* MIT Press. Freely available at `gaussianprocess.org/gpml `_. The reference text for the Gaussian-process regression used to build the surrogate models. Applications ------------ * **Mouris, K., Acuña Espinoza, E., Schwindt, S., Mohammadi, F., Haun, S., Wieprecht, S., & Oladyshkin, S. (2023).** *Stability Criteria for Bayesian Calibration of Reservoir Sedimentation Models.* Modeling Earth Systems and Environment, 9, 3643–3661. `doi:10.1007/s40808-023-01712-7 `_ Surrogate-assisted Bayesian calibration of a 2D hydro-morphodynamic reservoir sedimentation model. * **Schwindt, S., Callau Medrano, S., Mouris, K., Beckers, F., Haun, S., Nowak, W., Wieprecht, S., & Oladyshkin, S. (2023).** *Bayesian Calibration Points to Misconceptions in Three-Dimensional Hydrodynamic Reservoir Modeling.* Water Resources Research, 59(3), e2022WR033660. `doi:10.1029/2022WR033660 `_ Bayesian calibration of a 3D reservoir hydrodynamic model, showing how posterior geometry reveals faulty model assumptions. Software dependency ------------------- * **BayesValidRox** — `documentation `_. Used by HydroBayesCal for the experimental design and parameter sampling (``Input`` and ``ExpDesigns``).